Molecular eco-systems biology: towards an understanding of community function
Raes et al. (2008): Molecular eco-systems biology: towards an understanding of community function
This paper discusses "the necessary data types that are required to unite molecular microbiology and ecology to develop an understanding of community function" as well as "the potential shortcomings of these approaches".
What is a microbial ecosystem? The authors define:
A microbial ecosystem can be defined as a system that consists of all the microorganisms that live in a certain area or niche and that function together in the context of the other biotic (plants and animals) and abiotic (temperature, chemical composition and structure of the surroundings) factors of that niche. Communities range from being simple (for example, one- or two-species-dominated bioreactors and biofilms that are growing on ore-mine effluents or medical implants) to complex (for example, symbiotic human gut flora, plant rhizospheres, soil communities and ocean dwelling or even airborne microorganisms, such as those present in clouds). The complexity of the interactions in ecosystems depends on the number of species and the population structure, variation in food and energy supply and the geography of the habitat.
Why can computational systems biology help us study microbial ecosystems? The authors give the following answer:
Important issues that could be addressed by an ecosystems approach include estimating the relative importance of ecosystem members in ecosystem functioning and productivity, the effect of nutrient availability on species composition or the resilience of the ecosystem to disturbances.
Three aspects are important: "the 'parts list'; the connectivity between the parts; and the placement of connectivity in the context of time and space". About these three aspects, the authors write:
In single-organism systems biology, the parts list is generally established; almost 700 complete bacterial and archaeal genomes are available and some functional knowledge is available for approximately 70 80% of the encoded genes. For several model organisms, large-scale efforts have determined the connectivity among the parts (the physical and genetic interactions between genes). This, together with an ever increasing amount of temporal, spatial and structural data, means that model microorganism systems biology is ready to enter the third phase and progress towards its final goal the modelling and manipulation of complete organisms.
The paper further lists a lot of metagenomic approaches to obtaining the parts list such as environmental shotgun sequencing. Regarding the amount of data this has brought us, the authors write:
Metagenomic sequencing has so far added more than 10 billion bp to sequence databases. The larger projects usually sequence approximately 50 100 Mb per environment, which should provide a firm foundation to start investigating the functioning of the underlying communities. [...] [F]or most metagenomic samples, up to 75% of genes can be functionally characterized using targeted computational methodologies that combine homology and gene neighbourhood, and in simple communities, genes can be assigned to species (because complete genomes can be assembled), which means a parts list the proteins, their function and their host organism can be established.
The second aspect, connectivity, turns out to be far more complex in ecosystems than in single organisms:
In cellular systems, connectivity refers to protein protein interactions and modifications (such as phosphorylation), substrate and end-product transfer and regulatory interactions. In ecosystems, this concept encompasses an even wider range of interactions at various levels. These include ecological interactions between the carriers of function (organisms), such as competition, predation and structural interactions (such as mat formation).
"Data sources to probe connectivity" include "metabolic cooperation" and "cell-cell signalling - communication and quorum sensing". About each of these two topics the paper features one paragraph. Moreover, there is a chapter about "spatial and temporal variation".
The authors conclude:
Many datasets that will facilitate ecosystems biology are now being gathered. Metagenomics studies are collating the parts lists from which some general ecosystem properties, as well as first insights into metabolic cooperation, can be extracted. Other technologies that will gather additional, complementary data types, such as the environmental counterpart of high-throughput functional genomics (a cornerstone of cellular systems biology), are still in their infancy. However, technologies such as large-scale automated monitoring of chemicals and meta-metabolomics are developing rapidly.